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A model-based approach to forecasting corn and soybean yields

Keywords: Bayesian hierarchical model, Composite estimation, Model-based estimation, Survey sampling.

The National Agricultural Statistics Service (NASS) publishes forecasts and estimates of yields for several major crops. The yield forecasts have important economic implications through their effects on market prices and the World Agricultural Outlook Board’s supply and demand estimates. NASS collects information for forecasting corn yields from two sets of monthly surveys. A large scale farmer interview survey is conducted after the corn is harvested to determine the final yield estimate. Historically, yield forecasts are established by the Agricultural Statistics Board, a group of commodity specialists that convenes monthly and synthesizes the survey results with external sources information. Because of the subjective nature of the Board process, the traditional estimation procedures are not reproducible, are difficult to document precisely, and do not provide a measure of uncertainty. A model-based approach to corn and soybean yield forecasting is proposed as an alternative to the Board process. The model incorporates the direct survey estimates as well as external sources of information and produces a measure of statistical efficiency.